The CNN-based model (Figure REF .(c)) follows an encoder-decoder architecture of U-net network with skip connections [1]}. In this work, the encoder includes five convolutional layers. The first four convolutional layers are each followed by a downsampling layer that decrease the size of the output feature maps. The first two convolutional layer use two \(3\times 3\)  convolutions. The remaining convolutional layers use three \(3\times 3\)  convolutions. The encoder outputs 512 feature maps with a \(16\times 16\)  size. The decoder uses four upsampling layers each followed by a convolutional layer to construct an output image with the same resolution of the input one. The first two convolutional layers of decoder use three \(3\times 3\)  convolutions. The third convolutional layers of decoder use two \(3\times 3\)  convolutions. The last convolutional layer of decoder uses three \(3\times 3\)  convolutions to generate 64 feature maps with a \(256\times 256\)  size then uses a \(1\times 1\)  convolution to produce the finals 6 precipitation maps. The weights of the network are randomly initialized. Each \(3\times 3\)  convolution is followed by batch normalization and Leaky-ReLU non-linearity. The final \(1\times 1\)  convolution is followed by linear activation.
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